In a camera, the image formed at a focal plane where the film or image sensor is located may be blurred as a function of proximity to the optical axis of the camera. The further away a portion of an image is located from the optical axis (normally the center of the image), the more that portion may be blurred. The problem is exaggerated with images originating from inexpensive cameras, such as single-use cameras. Because of their simple optics or because the film may not be located in the position of best focus throughout the focal plane, single-use cameras tend to have significant sharpness loss with movement away from the optical axis toward the edges of the camera's image frame.
Image processing techniques have been designed to correct for field position dependent blur in digital images. Commonly-assigned U.S. Pat. No. 6,628,329 to Kelly et al., entitled “Correction of Position Dependent Blur in a Digital Image,” for example, teaches modifying the strength of a blur correcting algorithm (e.g., sharpening filter) as a function of position in a field, and is incorporated herein by reference. Such techniques tend to substantially improve perceived image quality. Even so, the application of such image processing techniques to a digital image may also increase noise in that image. Typically, the greater the amount of blur correction applied, the greater the amount of noise generated. Accordingly, the application of a blur correction algorithm that is field position dependent may enhance noise in a manner that is also field position dependent.
Several noise reducing algorithms such as speckle filtering, mean filtering, median filtering, local region filtering, and sigma filtering have been developed for reducing noise in digital images. In a typical sigma filtering algorithm, for example, the signal value of a pixel being filtered is replaced by a value which is determined by averaging the signal values of neighboring pixels that have values that lie within a fixed standard deviation range of the signal value of the pixel being filtered. This type of filtering is based on the assumption that noise occurs in a digital image with a Gaussian distribution so that effective noise suppression is possible within the standard deviation range. If there is a greater difference in signal value between a neighboring pixel and the filtered pixel, there is a high probability that this difference is not a result of noise, but is instead a result of some other content of the digital image. This neighboring pixel's signal value should, therefore, not be included in the averaging.
Nevertheless, such conventional noise reducing algorithms are not optimized to correct for noise intensities that are field position dependent. There is, as a result, a need for an effective method of correcting noise in digital images with noise intensities that vary as a function of field position.